Search Results for author: Zibin Zheng

Found 72 papers, 31 papers with code

FedBRB: An Effective Solution to the Small-to-Large Scenario in Device-Heterogeneity Federated Learning

no code implementations27 Feb 2024 Ziyue Xu, Mingfeng Xu, Tianchi Liao, Zibin Zheng, Chuan Chen

FedBRB can uses small local models to train all blocks of the large global model, and broadcasts the trained parameters to the entire space for faster information interaction.

Federated Learning

Clients Collaborate: Flexible Differentially Private Federated Learning with Guaranteed Improvement of Utility-Privacy Trade-off

no code implementations10 Feb 2024 Yuecheng Li, Tong Wang, Chuan Chen, Jian Lou, Bin Chen, Lei Yang, Zibin Zheng

This implies that our FedCEO can effectively recover the disrupted semantic information by smoothing the global semantic space for different privacy settings and continuous training processes.

Federated Learning

Training and Serving System of Foundation Models: A Comprehensive Survey

no code implementations5 Jan 2024 Jiahang Zhou, Yanyu Chen, Zicong Hong, Wuhui Chen, Yue Yu, Tao Zhang, Hui Wang, Chuanfu Zhang, Zibin Zheng

Additionally, the paper summarizes the challenges and presents a perspective on the future development direction of foundation model systems.

Rethinking and Simplifying Bootstrapped Graph Latents

1 code implementation5 Dec 2023 Wangbin Sun, Jintang Li, Liang Chen, Bingzhe Wu, Yatao Bian, Zibin Zheng

Graph contrastive learning (GCL) has emerged as a representative paradigm in graph self-supervised learning, where negative samples are commonly regarded as the key to preventing model collapse and producing distinguishable representations.

Contrastive Learning Self-Supervised Learning

The Devil is in the Data: Learning Fair Graph Neural Networks via Partial Knowledge Distillation

1 code implementation29 Nov 2023 Yuchang Zhu, Jintang Li, Liang Chen, Zibin Zheng

Experiments on several benchmark datasets demonstrate that FairGKD, which does not require access to demographic information, significantly improves the fairness of GNNs by a large margin while maintaining their utility.

Fairness Knowledge Distillation

LasTGL: An Industrial Framework for Large-Scale Temporal Graph Learning

no code implementations28 Nov 2023 Jintang Li, Jiawang Dan, Ruofan Wu, Jing Zhou, Sheng Tian, Yunfei Liu, Baokun Wang, Changhua Meng, Weiqiang Wang, Yuchang Zhu, Liang Chen, Zibin Zheng

Over the past few years, graph neural networks (GNNs) have become powerful and practical tools for learning on (static) graph-structure data.

Graph Learning

VeryFL: A Verify Federated Learning Framework Embedded with Blockchain

1 code implementation27 Nov 2023 Yihao Li, Yanyi Lai, Chuan Chen, Zibin Zheng

These mechanism on blockchain shows an underlying support of blockchain for federated learning to provide a verifiable training, aggregation and incentive distribution procedure and thus we named this framework VeryFL (A Verify Federated Learninig Framework Embedded with Blockchain).

Federated Learning

Tokenized Model: A Blockchain-Empowered Decentralized Model Ownership Verification Platform

no code implementations27 Nov 2023 Yihao Li, Yanyi Lai, Tianchi Liao, Chuan Chen, Zibin Zheng

By using the model watermarking technology, we point out the possibility of building a unified platform for model ownership verification.

Community-Aware Efficient Graph Contrastive Learning via Personalized Self-Training

no code implementations18 Nov 2023 Yuecheng Li, YanMing Hu, Lele Fu, Chuan Chen, Lei Yang, Zibin Zheng

However, for unsupervised and structure-related tasks such as community detection, current GCL algorithms face difficulties in acquiring the necessary community-level information, resulting in poor performance.

Community Detection Contrastive Learning +1

Contrastive Deep Nonnegative Matrix Factorization for Community Detection

1 code implementation4 Nov 2023 Yuecheng Li, Jialong Chen, Chuan Chen, Lei Yang, Zibin Zheng

Recently, nonnegative matrix factorization (NMF) has been widely adopted for community detection, because of its better interpretability.

Community Detection Contrastive Learning +3

Hetero$^2$Net: Heterophily-aware Representation Learning on Heterogenerous Graphs

no code implementations18 Oct 2023 Jintang Li, Zheng Wei, Jiawang Dan, Jing Zhou, Yuchang Zhu, Ruofan Wu, Baokun Wang, Zhang Zhen, Changhua Meng, Hong Jin, Zibin Zheng, Liang Chen

Through in-depth investigations on several real-world heterogeneous graphs exhibiting varying levels of heterophily, we have observed that heterogeneous graph neural networks (HGNNs), which inherit many mechanisms from GNNs designed for homogeneous graphs, fail to generalize to heterogeneous graphs with heterophily or low level of homophily.

Node Classification Representation Learning

Language Agents for Detecting Implicit Stereotypes in Text-to-image Models at Scale

no code implementations18 Oct 2023 Qichao Wang, Tian Bian, Yian Yin, Tingyang Xu, Hong Cheng, Helen M. Meng, Zibin Zheng, Liang Chen, Bingzhe Wu

The recent surge in the research of diffusion models has accelerated the adoption of text-to-image models in various Artificial Intelligence Generated Content (AIGC) commercial products.

SAILOR: Structural Augmentation Based Tail Node Representation Learning

1 code implementation13 Aug 2023 Jie Liao, Jintang Li, Liang Chen, Bingzhe Wu, Yatao Bian, Zibin Zheng

In the pursuit of promoting the expressiveness of GNNs for tail nodes, we explore how the deficiency of structural information deteriorates the performance of tail nodes and propose a general Structural Augmentation based taIL nOde Representation learning framework, dubbed as SAILOR, which can jointly learn to augment the graph structure and extract more informative representations for tail nodes.

Representation Learning

DsMtGCN: A Direction-sensitive Multi-task framework for Knowledge Graph Completion

no code implementations17 Jun 2023 Jining Wang, Chuan Chen, Zibin Zheng, Yuren Zhou

To solve the inherent incompleteness of knowledge graphs (KGs), numbers of knowledge graph completion (KGC) models have been proposed to predict missing links from known triples.

Entity Embeddings

Contextual Dictionary Lookup for Knowledge Graph Completion

no code implementations13 Jun 2023 Jining Wang, Delai Qiu, YouMing Liu, Yining Wang, Chuan Chen, Zibin Zheng, Yuren Zhou

We extend several KGE models with the method, resulting in substantial performance improvements on widely-used benchmark datasets.

Knowledge Graph Embedding Relation

Migrate Demographic Group For Fair GNNs

no code implementations7 Jun 2023 YanMing Hu, Tianchi Liao, Jialong Chen, Chuan Chen, Jing Bian, Zibin Zheng

To tackle this problem, we propose a brand new framework, FairMigration, which can dynamically migrate the demographic groups instead of keeping that fixed with raw sensitive attributes.

Fairness Graph Learning +1

Oversmoothing: A Nightmare for Graph Contrastive Learning?

1 code implementation3 Jun 2023 Jintang Li, Wangbin Sun, Ruofan Wu, Yuchang Zhu, Liang Chen, Zibin Zheng

Oversmoothing is a common phenomenon observed in graph neural networks (GNNs), in which an increase in the network depth leads to a deterioration in their performance.

Contrastive Learning

A Graph is Worth 1-bit Spikes: When Graph Contrastive Learning Meets Spiking Neural Networks

1 code implementation30 May 2023 Jintang Li, Huizhe Zhang, Ruofan Wu, Zulun Zhu, Baokun Wang, Changhua Meng, Zibin Zheng, Liang Chen

While contrastive self-supervised learning has become the de-facto learning paradigm for graph neural networks, the pursuit of higher task accuracy requires a larger hidden dimensionality to learn informative and discriminative full-precision representations, raising concerns about computation, memory footprint, and energy consumption burden (largely overlooked) for real-world applications.

Contrastive Learning Self-Supervised Learning

Attention Paper: How Generative AI Reshapes Digital Shadow Industry?

no code implementations26 May 2023 Qichao Wang, Huan Ma, WenTao Wei, Hangyu Li, Liang Chen, Peilin Zhao, Binwen Zhao, Bo Hu, Shu Zhang, Zibin Zheng, Bingzhe Wu

The rapid development of digital economy has led to the emergence of various black and shadow internet industries, which pose potential risks that can be identified and managed through digital risk management (DRM) that uses different techniques such as machine learning and deep learning.

Management

Less Can Be More: Unsupervised Graph Pruning for Large-scale Dynamic Graphs

1 code implementation18 May 2023 Jintang Li, Sheng Tian, Ruofan Wu, Liang Zhu, Welong Zhao, Changhua Meng, Liang Chen, Zibin Zheng, Hongzhi Yin

We approach the problem by our proposed STEP, a self-supervised temporal pruning framework that learns to remove potentially redundant edges from input dynamic graphs.

Dynamic Node Classification

Capturing Fine-grained Semantics in Contrastive Graph Representation Learning

no code implementations23 Apr 2023 Lin Shu, Chuan Chen, Zibin Zheng

Concretely, FSGCL first introduces a motif-based graph construction, which employs graph motifs to extract diverse semantics existed in graphs from the perspective of input data.

Contrastive Learning graph construction +1

HyperTuner: A Cross-Layer Multi-Objective Hyperparameter Auto-Tuning Framework for Data Analytic Services

1 code implementation20 Apr 2023 Hui Dou, Shanshan Zhu, Yiwen Zhang, Pengfei Chen, Zibin Zheng

Besides, experiments with different training datasets, different optimization objectives and different machine learning platforms verify that HyperTuner can well adapt to various data analytic service scenarios.

Can Perturbations Help Reduce Investment Risks? Risk-Aware Stock Recommendation via Split Variational Adversarial Training

no code implementations20 Apr 2023 Jiezhu Cheng, Kaizhu Huang, Zibin Zheng

By lowering the volatility of the stock recommendation model, SVAT effectively reduces investment risks and outperforms state-of-the-art baselines by more than 30% in terms of risk-adjusted profits.

Learning-To-Rank

Spectral Adversarial Training for Robust Graph Neural Network

1 code implementation20 Nov 2022 Jintang Li, Jiaying Peng, Liang Chen, Zibin Zheng, TingTing Liang, Qing Ling

In this work, we seek to address these challenges and propose Spectral Adversarial Training (SAT), a simple yet effective adversarial training approach for GNNs.

GANI: Global Attacks on Graph Neural Networks via Imperceptible Node Injections

1 code implementation23 Oct 2022 Junyuan Fang, Haixian Wen, Jiajing Wu, Qi Xuan, Zibin Zheng, Chi K. Tse

Specifically, to make the node injections as imperceptible and effective as possible, we propose a sampling operation to determine the degree of the newly injected nodes, and then generate features and select neighbors for these injected nodes based on the statistical information of features and evolutionary perturbations obtained from a genetic algorithm, respectively.

FedEgo: Privacy-preserving Personalized Federated Graph Learning with Ego-graphs

1 code implementation29 Aug 2022 Taolin Zhang, Chuan Chen, Yaomin Chang, Lin Shu, Zibin Zheng

As special information carriers containing both structure and feature information, graphs are widely used in graph mining, e. g., Graph Neural Networks (GNNs).

Federated Learning Graph Learning +2

Scaling Up Dynamic Graph Representation Learning via Spiking Neural Networks

1 code implementation15 Aug 2022 Jintang Li, Zhouxin Yu, Zulun Zhu, Liang Chen, Qi Yu, Zibin Zheng, Sheng Tian, Ruofan Wu, Changhua Meng

We explore a new direction in that we can capture the evolving dynamics of temporal graphs with spiking neural networks (SNNs) instead of RNNs.

Graph Representation Learning Node Classification

A Reinforcement Learning-based Offensive semantics Censorship System for Chatbots

no code implementations13 Jul 2022 Shaokang Cai, Dezhi Han, Zibin Zheng, Dun Li, NoelCrespi

In addition, by integrating a once-through learning approach, the speed of semantics purification is accelerated while reducing the impact on the quality of replies.

Chatbot Few-Shot Learning +2

A Survey of Deep Learning Models for Structural Code Understanding

1 code implementation3 May 2022 Ruoting Wu, Yuxin Zhang, Qibiao Peng, Liang Chen, Zibin Zheng

In recent years, the rise of deep learning and automation requirements in the software industry has elevated Intelligent Software Engineering to new heights.

FastGCL: Fast Self-Supervised Learning on Graphs via Contrastive Neighborhood Aggregation

no code implementations2 May 2022 Yuansheng Wang, Wangbin Sun, Kun Xu, Zulun Zhu, Liang Chen, Zibin Zheng

Graph contrastive learning (GCL), as a popular approach to graph self-supervised learning, has recently achieved a non-negligible effect.

Contrastive Learning Data Augmentation +3

GUARD: Graph Universal Adversarial Defense

1 code implementation20 Apr 2022 Jintang Li, Jie Liao, Ruofan Wu, Liang Chen, Zibin Zheng, Jiawang Dan, Changhua Meng, Weiqiang Wang

To mitigate such a threat, considerable research efforts have been devoted to increasing the robustness of GCNs against adversarial attacks.

Adversarial Defense

Distributed Evolution Strategies for Black-box Stochastic Optimization

no code implementations9 Apr 2022 Xiaoyu He, Zibin Zheng, Chuan Chen, Yuren Zhou, Chuan Luo, QIngwei Lin

This work concerns the evolutionary approaches to distributed stochastic black-box optimization, in which each worker can individually solve an approximation of the problem with nature-inspired algorithms.

Evolutionary Algorithms

HINNPerf: Hierarchical Interaction Neural Network for Performance Prediction of Configurable Systems

no code implementations8 Apr 2022 Jiezhu Cheng, Cuiyun Gao, Zibin Zheng

Due to the complex interactions among multiple options and the high cost of performance measurement under a huge configuration space, it is challenging to study how different configurations influence the system performance.

MMES: Mixture Model based Evolution Strategy for Large-Scale Optimization

1 code implementation15 Mar 2022 Xiaoyu He, Zibin Zheng, Yuren Zhou

This work provides an efficient sampling method for the covariance matrix adaptation evolution strategy (CMA-ES) in large-scale settings.

Recent Advances in Reliable Deep Graph Learning: Inherent Noise, Distribution Shift, and Adversarial Attack

no code implementations15 Feb 2022 Jintang Li, Bingzhe Wu, Chengbin Hou, Guoji Fu, Yatao Bian, Liang Chen, Junzhou Huang, Zibin Zheng

Despite the progress, applying DGL to real-world applications faces a series of reliability threats including inherent noise, distribution shift, and adversarial attacks.

Adversarial Attack Graph Learning

Neighboring Backdoor Attacks on Graph Convolutional Network

no code implementations17 Jan 2022 Liang Chen, Qibiao Peng, Jintang Li, Yang Liu, Jiawei Chen, Yong Li, Zibin Zheng

To address such a challenge, we set the trigger as a single node, and the backdoor is activated when the trigger node is connected to the target node.

Backdoor Attack

Understanding Structural Vulnerability in Graph Convolutional Networks

1 code implementation13 Aug 2021 Liang Chen, Jintang Li, Qibiao Peng, Yang Liu, Zibin Zheng, Carl Yang

In this work, we theoretically and empirically demonstrate that structural adversarial examples can be attributed to the non-robust aggregation scheme (i. e., the weighted mean) of GCNs.

A Decentralized Federated Learning Framework via Committee Mechanism with Convergence Guarantee

1 code implementation1 Aug 2021 Chunjiang Che, XiaoLi Li, Chuan Chen, Xiaoyu He, Zibin Zheng

In addition, we theoretically analyze and prove the convergence of CMFL under different election and selection strategies, which coincides with the experimental results.

Federated Learning

FedGL: Federated Graph Learning Framework with Global Self-Supervision

no code implementations7 May 2021 Chuan Chen, Weibo Hu, Ziyue Xu, Zibin Zheng

Moreover, the global self-supervision enables the information of each client to flow and share in a privacy-preserving manner, thus alleviating the heterogeneity and utilizing the complementarity of graph data among different clients.

Federated Learning Graph Learning +2

GraphGallery: A Platform for Fast Benchmarking and Easy Development of Graph Neural Networks Based Intelligent Software

1 code implementation16 Feb 2021 Jintang Li, Kun Xu, Liang Chen, Zibin Zheng, Xiao Liu

Graph Neural Networks (GNNs) have recently shown to be powerful tools for representing and analyzing graph data.

Benchmarking

A_Blockchain-Based_Decentralized_Federated_Learning_Framework_with_Committee_Consensus

no code implementations IEEE Network 2021 Yuzheng Li, Chuan Chen, Nan Liu, Huawei Huang, Zibin Zheng, and Qiang Yan

To address these security issues, we propose a decentralized federated learning framework based on blockchain, that is, a Blockchain- based Federated Learning framework with Committee consensus (BFLC).

Federated Learning

Tensor Completion via Convolutional Sparse Coding Regularization

no code implementations2 Dec 2020 Zhebin Wu, Tianchi Liao, Chuan Chen, Cong Liu, Zibin Zheng, Xiongjun Zhang

On the contrary, in the field of signal processing, Convolutional Sparse Coding (CSC) can provide a good representation of the high-frequency component of the image, which is generally associated with the detail component of the data.

MG-GCN: Fast and Effective Learning with Mix-grained Aggregators for Training Large Graph Convolutional Networks

no code implementations17 Nov 2020 Tao Huang, Yihan Zhang, Jiajing Wu, Junyuan Fang, Zibin Zheng

To tackle the dilemma between accuracy and efficiency, we propose to use aggregators with different granularities to gather neighborhood information in different layers.

Ensemble Knowledge Distillation for CTR Prediction

no code implementations8 Nov 2020 Jieming Zhu, Jinyang Liu, Weiqi Li, Jincai Lai, Xiuqiang He, Liang Chen, Zibin Zheng

Recently, deep learning-based models have been widely studied for click-through rate (CTR) prediction and lead to improved prediction accuracy in many industrial applications.

Click-Through Rate Prediction Knowledge Distillation

Adversarial Attack on Large Scale Graph

1 code implementation8 Sep 2020 Jintang Li, Tao Xie, Liang Chen, Fenfang Xie, Xiangnan He, Zibin Zheng

Currently, most works on attacking GNNs are mainly using gradient information to guide the attack and achieve outstanding performance.

Adversarial Attack

Outlier-Resilient Web Service QoS Prediction

1 code implementation1 Jun 2020 Fanghua Ye, Zhiwei Lin, Chuan Chen, Zibin Zheng, Hong Huang

The proliferation of Web services makes it difficult for users to select the most appropriate one among numerous functionally identical or similar service candidates.

A Blockchain-based Decentralized Federated Learning Framework with Committee Consensus

1 code implementation2 Apr 2020 Yuzheng Li, Chuan Chen, Nan Liu, Huawei Huang, Zibin Zheng, Qiang Yan

To address these security issues, we proposed a decentralized federated learning framework based on blockchain, i. e., a Blockchain-based Federated Learning framework with Committee consensus (BFLC).

Federated Learning

XBlock-EOS: Extracting and Exploring Blockchain Data From EOSIO

no code implementations26 Mar 2020 Weilin Zheng, Zibin Zheng, Hong-Ning Dai, Xu Chen, PeiLin Zheng

It is challenging to process and analyze a high volume of raw EOSIO data and establish the mapping from original raw data to the well-grained datasets since it requires substantial efforts in extracting various types of data as well as sophisticated knowledge on software engineering and data analytics.

Computational Engineering, Finance, and Science Cryptography and Security

Modelling High-Order Social Relations for Item Recommendation

no code implementations23 Mar 2020 Yang Liu, Liang Chen, Xiangnan He, Jiaying Peng, Zibin Zheng, Jie Tang

The prevalence of online social network makes it compulsory to study how social relations affect user choice.

Vocal Bursts Intensity Prediction

An Uncoupled Training Architecture for Large Graph Learning

no code implementations21 Mar 2020 Dalong Yang, Chuan Chen, Youhao Zheng, Zibin Zheng, Shih-wei Liao

Instead of directly processing the coupled nodes as GCNs, Node2Grids supports a more efficacious method in practice, mapping the coupled graph data into the independent grid-like data which can be fed into the efficient Convolutional Neural Network (CNN).

Graph Learning

A Survey of Adversarial Learning on Graphs

2 code implementations10 Mar 2020 Liang Chen, Jintang Li, Jiaying Peng, Tao Xie, Zengxu Cao, Kun Xu, Xiangnan He, Zibin Zheng, Bingzhe Wu

To bridge this gap, we investigate and summarize the existing works on graph adversarial learning tasks systemically.

Clustering Graph Clustering +2

Prophet: Proactive Candidate-Selection for Federated Learning by Predicting the Qualities of Training and Reporting Phases

no code implementations3 Feb 2020 Huawei Huang, Kangying Lin, Song Guo, Pan Zhou, Zibin Zheng

In the dynamic environment, the mobile devices selected by the existing reactive candidate-selection algorithms very possibly fail to complete the training and reporting phases of FL, because the FL parameter server only knows the currently-observed resources of all candidates.

Federated Learning Test

PIRATE: A Blockchain-based Secure Framework of Distributed Machine Learning in 5G Networks

no code implementations17 Dec 2019 Sicong Zhou, Huawei Huang, Wuhui Chen, Zibin Zheng, Song Guo

Therefore, to provide the byzantine-resilience for distributed learning in 5G era, this article proposes a secure computing framework based on the sharding-technique of blockchain, namely PIRATE.

Distributed, Parallel, and Cluster Computing Cryptography and Security

Blockchain Intelligence: When Blockchain Meets Artificial Intelligence

no code implementations11 Dec 2019 Zibin Zheng, Hong-Ning Dai, Jiajing Wu

Blockchain is gaining extensive attention due to its provision of secure and decentralized resource sharing manner.

Data Poisoning Attacks on Neighborhood-based Recommender Systems

no code implementations1 Dec 2019 Liang Chen, Yangjun Xu, Fenfang Xie, Min Huang, Zibin Zheng

2) the fake users can be transferred to attack the state-of-the-art collaborative filtering recommender systems such as Neural Collaborative Filtering and Bayesian Personalized Ranking Matrix Factorization.

Collaborative Filtering Data Poisoning +1

XBlock-ETH: Extracting and Exploring Blockchain Data From Ethereum

no code implementations1 Nov 2019 PeiLin Zheng, Zibin Zheng, Hong-Ning Dai

We name these well-processed Ethereum datasets as XBlock-ETH, which consists of the data of blockchain transactions, smart contracts, and cryptocurrencies (i. e., tokens).

Cryptography and Security

Selecting Reliable Blockchain Peers via Hybrid Blockchain Reliability Prediction

1 code implementation31 Oct 2019 PeiLin Zheng, Zibin Zheng, Liang Chen

Blockchain and blockchain-based decentralized applications are attracting increasing attentions recently.

Software Engineering Distributed, Parallel, and Cluster Computing

Logzip: Extracting Hidden Structures via Iterative Clustering for Log Compression

1 code implementation24 Sep 2019 Jinyang Liu, Jieming Zhu, Shilin He, Pinjia He, Zibin Zheng, Michael R. Lyu

Data compression is essential to reduce the cost of log storage.

Databases Software Engineering

Big Data Analytics for Large Scale Wireless Networks: Challenges and Opportunities

no code implementations2 Sep 2019 Hong-Ning Dai, Raymond Chi-Wing Wong, Hao Wang, Zibin Zheng, Athanasios V. Vasilakos

We then present a detailed survey of the technical solutions to the challenges in BDA for large scale wireless networks according to each stage in the life cycle of BDA.

T-EDGE: Temporal WEighted MultiDiGraph Embedding for Ethereum Transaction Network Analysis

1 code implementation13 May 2019 Jiajing Wu, Dan Lin, Zibin Zheng, Qi Yuan

By taking the realistic rules and features of transaction networks into consideration, we first model the Ethereum transaction network as a Temporal Weighted Multidigraph (TWMDG), where each node is a unique Ethereum account and each edge represents a transaction weighted by amount and assigned with timestamp.

Social and Information Networks Applications

Tools and Benchmarks for Automated Log Parsing

6 code implementations8 Nov 2018 Jieming Zhu, Shilin He, Jinyang Liu, Pinjia He, Qi Xie, Zibin Zheng, Michael R. Lyu

Logs are imperative in the development and maintenance process of many software systems.

Software Engineering

Deep Autoencoder-like Nonnegative Matrix Factorization for Community Detection

2 code implementations CIKM 2018 Fanghua Ye, Chuan Chen, Zibin Zheng

Considering the complicated and diversified topology structures of real-world networks, it is highly possible that the mapping between the original network and the community membership space contains rather complex hierarchical information, which cannot be interpreted by classic shallow NMF-based approaches.

Local Community Detection Network Community Partition +2

Collaborative Deep Learning Across Multiple Data Centers

no code implementations16 Oct 2018 Kele Xu, Haibo Mi, Dawei Feng, Huaimin Wang, Chuan Chen, Zibin Zheng, Xu Lan

Valuable training data is often owned by independent organizations and located in multiple data centers.

Learning Semantic Representations for Unsupervised Domain Adaptation

1 code implementation ICML 2018 Shaoan Xie, Zibin Zheng, Liang Chen, Chuan Chen

Prior domain adaptation methods address this problem through aligning the global distribution statistics between source domain and target domain, but a drawback of prior methods is that they ignore the semantic information contained in samples, e. g., features of backpacks in target domain might be mapped near features of cars in source domain.

Learning Semantic Representations Unsupervised Domain Adaptation

A Directed Acyclic Graph Approach to Online Log Parsing

no code implementations12 Jun 2018 Pinjia He, Jieming Zhu, Pengcheng Xu, Zibin Zheng, Michael R. Lyu

A typical log-based system reliability management procedure is to first parse log messages because of their unstructured format; and apply data mining techniques on the parsed logs to obtain critical system behavior information.

Software Engineering

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